Brain activity is commonly determined from electroencephalograms (EEG) measurements from multiple electrodes positioned on scalp sites over the subject's head, with signals from the electrodes fed to an EEG data collection system. Following artifact rejection (typically performed), signal analysis of electroencephalograms (EEG) measurements using short-term Fourier analysis or wavelet analysis produces a time-frequency spectral data analysis for the sites. The spectral results may be decomposed into spectrum band power; spectrum coherence computed from the power spectral matrix; and causality for the coherence between the sites as a network such as Granger causality.
Further refinement in decompositions where there is spectrum coherence (as a measure of mutual synchronicity among sites), may be decomposed into different measures of the Granger causality for the direction of information flow among sites. These measures include the directed coherence (DC), which is defined as the ratio of the spectral transfer function between two sites, and the square root of the auto power of one of the sites; and still further, the directed transfer function (DTF).
In further developments, graph theory measures are applied for analysis of the sites as nodes of a network, by using small world network metrics computed from the cross-correlation matrices for the sites, such as node degree (average number of connections nodes), clustering coefficient (ratio of existing connections to all possible), diameter (shortest path between nodes), and efficiency (measure of number of parallel connections among nodes), among others. In experimental studies, statistical analysis may be applied to these measures by treatments for study results.
While these conventional methods are of interest to the research community, they are commonly of low statistical power as shown by sometimes conflicts in replication of study results. This is because the statistics used in these studies analyzes the power spectrums for the sites and the coherences between the sites (or derivations thereof), as separate statistical measures. This conventional methodology can result in a large number of measures; for instance, there are at least 2030 separate measures for a study with a 64-electrode scalp site EEG data collection system (an analysis of signals from N scalp sites conducted separately would involve N-power spectrums and N*(N−1)/2 coherence spectrums). Furthermore, the analyses are commonly conducted separately by frequency bands of which there are at least four considered in the EEG spectrum: delta, alpha, beta, and gamma, although the study may be limited to a single band. This large number of measures severally reduces the overall statistical power of any analysis and increases the family-wise Type I error (that is, error in accepting the analysis as significant). Of further concern is that these measures are all from same data source and being highly correlated are redundantly a single measure; it is suspect to include all as separate dependent measures in conventional statistical methods (such as multiple analysis of variance), thereby increasing the probability that results are incorrectly significant by chance alone.
Therefore, if measurement of electroencephalograms (EEG) with a scalp site electrode EEG data collection system is to be useful in real-life applications (such as in moving vehicles with operator control), there is a need in the art for a method and apparatus for generating a global measure for electroencephalograms (EEG) analysis. Further, there is an advantage in the extension of such a global measure to cerebral sources of the scalp site electroencephalograms (EEG), with the sources located by cortical structure that form cerebral networks relatable to cognitive functions.